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import torch
import torch.nn as nn
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from typing import Optional
from torch import Tensor
from torch.nn import functional as F
from .configuration_projector import ProjectorConfig_OMG_LLaVA
class Naive_Proj(nn.Module):
def __init__(self, config, rm_prior_embedding=False,
rm_query=False):
super().__init__()
query_channels = config.query_channels
self.query_channels = query_channels
feat_channels = config.feat_channels
if isinstance(query_channels, tuple):
query_channels = query_channels[0]
if isinstance(feat_channels, tuple):
feat_channels = feat_channels[0]
add_cross_attn_layer = config.add_cross_attn_layer
self.add_cross_attn_layer = config.add_cross_attn_layer
query_channels = query_channels * 2 # feat + embed
self.query_proj = nn.Linear(query_channels, feat_channels)
modules = [
nn.Linear(
feat_channels,
config.llm_hidden_size,
bias=config.bias)
]
for _ in range(1, config.depth):
modules.append(ACT2FN[config.hidden_act])
modules.append(
nn.Linear(
config.llm_hidden_size,
config.llm_hidden_size,
bias=config.bias))
self.model = nn.Sequential(*modules)
if add_cross_attn_layer:
print("Using Cross Attention Layer at Projector !!!")
self.query_cross_attn = CrossAttentionLayer(
d_model=config.llm_hidden_size,
nhead=32,
)
self.query_ffn = FFNLayer(
d_model=config.llm_hidden_size,
dim_feedforward=4096,
)
else:
self.query_cross_attn = None
self.query_ffn = None
modules = [
nn.Linear(
feat_channels + query_channels,
config.llm_hidden_size,
bias=config.bias)
]
for _ in range(1, config.depth):
modules.append(ACT2FN[config.hidden_act])
modules.append(
nn.Linear(
config.llm_hidden_size,
config.llm_hidden_size,
bias=config.bias))
self.model_feat = nn.Sequential(*modules)
self.seperate_embed = nn.Embedding(1, config.llm_hidden_size)
self.rm_prior_embedding = rm_prior_embedding
self.rm_query = rm_query
visual_prompt_proj = config.visual_prompt_proj
self.visual_prompt_proj = visual_prompt_proj
if not visual_prompt_proj:
self.visual_prompt_query_proj = None
self.visual_prompt_query_model = None
self.visual_prompt_query_cross_attn = None
self.visual_prompt_query_ffn = None
else:
print("Initialized all Layers for Visual Prompt in Projector !!!")
self.visual_prompt_query_proj = nn.Linear(query_channels, feat_channels)
modules = [
nn.Linear(
feat_channels,
config.llm_hidden_size,
bias=config.bias)
]
for _ in range(1, config.depth):
modules.append(ACT2FN[config.hidden_act])
modules.append(
nn.Linear(
config.llm_hidden_size,
config.llm_hidden_size,
bias=config.bias))
self.visual_prompt_query_model = nn.Sequential(*modules)
if add_cross_attn_layer:
self.visual_prompt_query_cross_attn = CrossAttentionLayer(
d_model=config.llm_hidden_size,
nhead=32,
)
self.visual_prompt_query_ffn = FFNLayer(
d_model=config.llm_hidden_size,
dim_feedforward=4096,
)
else:
self.visual_prompt_query_cross_attn = None
self.visual_prompt_query_ffn = None
def forward(self, x):
clip_feature, query_feat, attention_mask = x
query_feat_copy = query_feat[0, :1] # (1, 1, c)
# clip feature (bs, hw, c + 2 * q_c)
# query_feat (bs, q, c)
# attention_mask (bs, q, hw)
if self.rm_prior_embedding:
clip_feature_feat = clip_feature[:, :, :-512]
clip_feature_query = clip_feature[:, :, -512:] * 0.0
clip_feature = torch.cat([clip_feature_feat, clip_feature_query], dim=-1)
query_feat = self.query_proj(query_feat)
valid_mask = attention_mask.sum(dim=-1) < attention_mask.shape[-1] # (bs, q)
# valid_mask # (bs, q)
# query_feat (bs, q, c)
# clip_feature (bs, hw, c)
# attn_map (bs, q, hw)
bs, n_q = query_feat.shape[:2]
layer_outputs = self.model(query_feat)
# filter
clip_feature_out = clip_feature
clip_feature_out = self.model_feat(clip_feature_out)
ret = []
valid_queries_embeddings = []
for layer_output, keep in zip(layer_outputs, valid_mask):
valid_queries_embeddings.append(layer_output[keep])
self.valid_queries_embeddings = valid_queries_embeddings
self.last_clip_feature = clip_feature_out
for clip_feat, layer_output, keep in zip(clip_feature_out, layer_outputs, valid_mask):
valid_layer_output = layer_output[keep]
if self.add_cross_attn_layer:
valid_layer_output = self.query_cross_attn(
valid_layer_output.unsqueeze(1), clip_feat.unsqueeze(1),
)[:, 0]
valid_layer_output = self.query_ffn(valid_layer_output)
if self.rm_query:
ret.append(clip_feat + torch.mean(self.seperate_embed.weight) * 0.0 + torch.mean(valid_layer_output) * 0.0)
else:
ret.append(torch.cat([clip_feat, self.seperate_embed.weight, valid_layer_output], dim=0))
# generate zero using visual prompt projector if valid
if self.visual_prompt_proj:
visual_prompt_embeddings = query_feat_copy.to(self.visual_prompt_query_proj.weight.dtype)
visual_prompt_embeddings = self.visual_prompt_query_proj(visual_prompt_embeddings)
visual_prompt_embeddings = self.visual_prompt_query_model(visual_prompt_embeddings) # (B, C)
if self.add_cross_attn_layer:
clip_feat = self.last_clip_feature[0] # (B, HW, C)
visual_prompt_embeddings = self.visual_prompt_query_cross_attn(
visual_prompt_embeddings.unsqueeze(1), clip_feat.unsqueeze(1),
)[:, 0]
visual_prompt_embeddings = self.visual_prompt_query_ffn(visual_prompt_embeddings)
self.visual_prompt_zero = visual_prompt_embeddings.sum() * 0.0
else:
self.visual_prompt_zero = 0.0
return ret
def forward_visual_prompts_embeddings(self, visual_prompt_embeddings, batch_idxs):
if self.visual_prompt_proj:
visual_prompt_embeddings = visual_prompt_embeddings.to(self.visual_prompt_query_proj.weight.dtype)
visual_prompt_embeddings = self.visual_prompt_query_proj(visual_prompt_embeddings)
visual_prompt_embeddings = self.visual_prompt_query_model(visual_prompt_embeddings) # (B, C)
if self.add_cross_attn_layer:
clip_feat = self.last_clip_feature[batch_idxs].permute(1, 0, 2) # (B, HW, C)
visual_prompt_embeddings = self.visual_prompt_query_cross_attn(
visual_prompt_embeddings.unsqueeze(0), clip_feat,
)[0, :]
visual_prompt_embeddings = self.visual_prompt_query_ffn(visual_prompt_embeddings)
else:
visual_prompt_embeddings = visual_prompt_embeddings.to(self.query_proj.weight.dtype)
visual_prompt_embeddings = self.query_proj(visual_prompt_embeddings)
visual_prompt_embeddings = self.model(visual_prompt_embeddings) # (B, C)
if self.add_cross_attn_layer:
clip_feat = self.last_clip_feature[batch_idxs].permute(1, 0, 2) # (B, HW, C)
visual_prompt_embeddings = self.query_cross_attn(
visual_prompt_embeddings.unsqueeze(0), clip_feat,
)[0, :]
visual_prompt_embeddings = self.query_ffn(visual_prompt_embeddings)
return visual_prompt_embeddings
def init_visual_prompt_weights(self):
if self.visual_prompt_query_proj is not None:
self.visual_prompt_query_proj.load_state_dict(self.query_proj.state_dict())
if self.visual_prompt_query_model is not None:
self.visual_prompt_query_model.load_state_dict(self.model.state_dict())
if self.visual_prompt_query_cross_attn is not None:
self.visual_prompt_query_cross_attn.load_state_dict(
self.query_cross_attn.state_dict()
)
if self.visual_prompt_query_ffn is not None:
self.visual_prompt_query_ffn.load_state_dict(
self.query_ffn.state_dict()
)
return
class ProjectorModel_OMG_LLaVA(PreTrainedModel):
_auto_class = 'AutoModel'
config_class = ProjectorConfig_OMG_LLaVA
base_model_prefix = 'model'
supports_gradient_checkpointing = True
def __init__(self, config: ProjectorConfig_OMG_LLaVA) -> None:
super().__init__(config)
self.gradient_checkpointing = False
self.rm_prior_embedding = False
self.rm_query = False
self.model = Naive_Proj(config, )
def enable_input_require_grads(self):
def make_inputs_require_grad(module, input, output):
if isinstance(output, torch.Tensor):
output.requires_grad_(True)
else:
for item in output:
item.requires_grad_(True)
self.model.register_forward_hook(make_inputs_require_grad)
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, ProjectorConfig_OMG_LLaVA):
module.gradient_checkpointing = value
def forward(self, x):
if self.gradient_checkpointing and self.training:
layer_outputs = torch.utils.checkpoint.checkpoint(self.model, x)
else:
layer_outputs = self.model(x)
return layer_outputs
class SelfAttentionLayer(nn.Module):
def __init__(self, d_model, nhead, dropout=0.0,
activation="relu", normalize_before=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(self, tgt,
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
q = k = self.with_pos_embed(tgt, query_pos)
tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt
def forward_pre(self, tgt,
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
tgt2 = self.norm(tgt)
q = k = self.with_pos_embed(tgt2, query_pos)
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout(tgt2)
return tgt
def forward(self, tgt,
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
if self.normalize_before:
return self.forward_pre(tgt, tgt_mask,
tgt_key_padding_mask, query_pos)
return self.forward_post(tgt, tgt_mask,
tgt_key_padding_mask, query_pos)
class CrossAttentionLayer(nn.Module):
def __init__(self, d_model, nhead, dropout=0.0,
activation="relu", normalize_before=False):
super().__init__()
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(self, tgt, memory,
memory_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
key=self.with_pos_embed(memory, pos),
value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)[0]
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt
def forward_pre(self, tgt, memory,
memory_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
tgt2 = self.norm(tgt)
tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
key=self.with_pos_embed(memory, pos),
value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)[0]
tgt = tgt + self.dropout(tgt2)
return tgt
def forward(self, tgt, memory,
memory_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
if self.normalize_before:
return self.forward_pre(tgt, memory, memory_mask,
memory_key_padding_mask, pos, query_pos)
return self.forward_post(tgt, memory, memory_mask,
memory_key_padding_mask, pos, query_pos)
class FFNLayer(nn.Module):
def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,
activation="relu", normalize_before=False):
super().__init__()
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm = nn.LayerNorm(d_model)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(self, tgt):
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt
def forward_pre(self, tgt):
tgt2 = self.norm(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout(tgt2)
return tgt
def forward(self, tgt):
if self.normalize_before:
return self.forward_pre(tgt)
return self.forward_post(tgt)
def _get_activation_fn(activation):
"""Return an activation function given a string"""
if activation == "relu":
return F.relu
if activation == "gelu":
return F.gelu
if activation == "glu":
return F.glu
raise RuntimeError(F"activation should be relu/gelu, not {activation}.") |